Many financial services firms maintain a Fixed Income Division (“FID”). FIDs are increasingly booking products across disparate business units. For example, Commodities, Corporates, and Forex (“FX”) (an over-the-counter “OTC” market where buyers and sellers conduct foreign exchange transactions) all maintain interest-rate swaps.
In a transaction or deal, certain market parameters may be tracked to determine, among other things, fair market value of the deal. The parameters may be interest rates, bond yields, values of similar deals and the like. These parameters are known as market observables. To value a particular transaction or deal, it may be necessary to identify a mathematical function based upon the observables such that a fair market value for the deal or transaction may be derived.
After determining a mathematical function to derive a value of a transaction or deal, it is often desirable to measure risk associated with the various investment instruments that comprise the transaction or deal. One method for determining the risk involved in a particular deal or transaction is to measure the sensitivity of the fair market value to the market observables. The sensitivity analysis may be computed by taking the partial derivative of the mathematical function representing the deal or transaction over the particular market observable, as is known in the art.
For a given transaction or deal there may be many sensitivities that need to be calculated and tracked. Common examples include: delta, the change in price of a call option for every one-point move in price of the underlying security; gamma, a measurement of how fast delta changes; kappa, a value representing the expected change in the price of an option in response to a 1% change in the volatility of the underlying stock. A set of these sensitivities comprise risk for the deal or transaction. Calculating the risk may be necessary to determine exposure to the various market forces of the deal or transaction, which in turn assists in determining an effective hedging strategy.
The current trend in the financial industry is to book deals or transactions across various business units to accurately track the risk involved in the deal as well as effectively hedge the deal. As the deals become more complex, the mathematical functions used to model them become more complex. As deals expand, the inputs into the mathematical function become more complex. The result is that the computation of sensitivities becomes more complex, thus leading to complex risk calculations. In some instances, many deals are rolled up into one book or many books. It may be necessary to compute the sensitivity of the book to one market observable, thereby measuring the sensitivity of several deals across one market observable. Such a process is known in the industry as aggregate risk reporting.
This complexity demands integration of IT systems across various product lines. In some instances, the demands are being met by a variety of ad-hoc solutions which include double booking by traders of trades into two different systems or developing special hybrid programs to extract risk data from one system and reformat it for another, for example. If the demands are not met, traders may be prevented from doing cross-product trades because of lack of system support.
Many financial services firms employ relational databases to handle storing the various parameters related to deals or transactions. However, many of these systems may not be robust enough for risk reporting. A typical trader may need to view the risk for all products in a book aggregated together, as noted above. The task is complicated by the fact that the risk numbers for different products need to be converted to a compatible format, and the different traders may want different formats for the same product. For example, a swap trader may want to see interest rate risk for both swaps and bonds against a LIBOR yield curve, while a bond trader may want to see both of them against bond yields.
A typical relational database may not be easily scalable to support permutations of the sensitivity analysis for various business units and therefore may preclude proper risk reporting. This may lead to various business units programming separate databases to support their needs. As this occurs, the financial services firm loses its ability to integrate and aggregate across product lines in an efficient manner. This leads to improper risk reporting and an inability to hedge large books in a timely manner.